Lead Scientist, Data Science

About XPO India Data Science Team

XPO India Shared Services is seeking an experienced Lead Data Scientist to join their Data Science team based in Pune or Hyderabad. This role requires a veteran data science professional with over seven years of experience, specializing in advanced AI technologies and machine learning engineering.

Role Overview

The Lead Data Scientist will champion the development and deployment of sophisticated machine learning models, focusing on Generative AI and Retrieval-Augmented Generation (RAG) methods. This position entails leading the entire ML model lifecycle while also overseeing scalable and secure AI production deployments through strong MLOps practices.

Responsibilities

• Design, develop, and deploy cutting-edge machine learning and AI models, prominently Generative AI and RAG architectures.
• Manage comprehensive model lifecycle tasks including data preprocessing, feature engineering, model training, assessment, deployment, and ongoing monitoring.
• Build and enhance MLOps pipelines ensuring continuous integration, delivery, and operational monitoring of ML models.
• Apply best practices for model governance, reproducibility, and scalability in production environments.
• Work closely with engineering divisions to seamlessly integrate AI models into live systems.
• Lead, mentor, and support data scientists and ML engineers, fostering a culture of technical excellence and innovation.
• Collaborate with stakeholders to convert complex AI solutions into tangible business benefits.
• Evaluate and adopt emerging AI technologies and frameworks to drive organizational advancements.
• Assure models comply with performance standards, reliability, and regulatory requirements.
• Develop systems to detect model drift, performance degradation, and bias.
• Advocate for responsible AI implementations with a strong focus on security and ethical standards.

Required Skills & Expertise

• Demonstrated proficiency in Generative AI technologies including large language models, diffusion models, and transformer architectures.
• Experience implementing and managing Retrieval-Augmented Generation (RAG) pipelines.
• Strong knowledge of MLOps practices such as CI/CD pipelines for machine learning, model monitoring, and orchestration tools like MLflow, Kubeflow, and Airflow.
• Expertise in ML engineering utilizing Python along with frameworks like PyTorch and TensorFlow, including distributed training on cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Familiarity with vector database technologies such as Pinecone, Weaviate, and FAISS, and retrieval systems.